Noise Resistant Morphological Algorithm of Moving Forklift Truck Detection on Noisy Image Data

In this paper the authors focus on the specific problem of machine vision, namely, the video-based detection of the moving forklift truck. It is shown that the detection quality of the state-of-the-art local descriptors SURF, SIFT, etc. is not satisfactory if the resolution is low and the illumination is changed dramatically. The authors propose a novel algorithm to detect the presence of a cargo on the forklift truck on the basis of the mathematical morphological operators. At first, the movement direction is estimated with the updating motion history image method and the front part of the moving object is obtained. Next, contours are detected and the morphological operations in front of the moving object are used to compute several geometric features of an empty forklift. In the experimental study, it has been shown that the proposed method has 40% lower false positive rate and 27% lower false negative rate in comparison with conventional matching of local descriptors. Moreover, this algorithm is 7-35 times faster.